Simulating hydrologic pathway contributions in fluvial and karst settings: An evaluation of conceptual, physically-based, and deep learning modeling approaches-Reference-Cited by-同舟云学术

Simulating hydrologic pathway contributions in fluvial and karst settings: An evaluation of conceptual, physically-based, and deep learning modeling approaches

Published:2022-12 Issue: Volume:17 Page:100134
ISSN:2589-9155
Container-title:Journal of Hydrology X
language:en
Short-container-title:Journal of Hydrology X

Author:

Husic Admin,Al-Aamery Nabil,Fox James F.

Publisher

Elsevier BV

Subject

Water Science and Technology

Reference52 articles.

1. Evaluation of climate modeling factors impacting the variance of streamflow;Al Aamery;J. Hydrol.,2016

2. Variance analysis of forecasted streamflow maxima in a wet temperate climate;Al Aamery;J. Hydrol.,2018

3. Numerical model development for investigating hydrologic pathways in shallow fluviokarst;Al Aamery;J. Hydrol.,2021

4. Uncertainty quantification for hydrological models based on neural networks: the dropout ensemble;Althoff;Stoch. Environ. Res. Risk Assess.,2021

5. Simulation of karst spring discharge using a combination of time–frequency analysis methods and long short-term memory neural networks;An;J. Hydrol.,2020

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